Battery cycle life prediction method, product and electronic equipment

Abstract

The invention discloses a battery cycle life prediction method, a product and electronic equipment, and belongs to the technical field of new energy batteries, and the method comprises the following steps: selecting a plurality of battery cells for cycle life test, and recording the capacity of each battery cell; selecting the battery cell with the lowest capacity as a reference battery cell, taking the number of cycles as a reference number of cycles or the cycle time as a reference time, and taking the capacity change as # imgabs0 #; other battery cells are selected as to-be-measured battery cells, battery capacity change data of the first j circles of the to-be-measured battery cells are obtained, and the circulation circle number difference value or the circulation time difference value of the to-be-measured battery cells and the reference battery cell is calculated; calculating a function relationship between a cycle number difference value or a cycle time difference value of the to-be-detected battery cell and a reference battery cell # imgabs1 #; obtaining a corresponding Cyclediff relational expression of the battery cell to be tested in the reference SOH range; and calculating the SOH of the to-be-detected battery cell, and obtaining the number of cycles or cycle time of the to-be-detected battery cell under different SOHs. The cycle life of the battery can be predicted by using a small amount of test data.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy batteries, and particularly to a method, product and electronic device for predicting the cycle life of a battery. Background Art

[0002] With the rapid development of the new energy industry and the promotion of China's energy storage policies, energy storage power stations represented by lithium ions have shown explosive installation in recent years, with an increasingly large installation scale. At the same time, some problems in battery management have become more and more numerous. Among them, during the use of lithium-ion batteries, due to reasons such as the decomposition of battery materials, the drying up of electrolytes, and the loss of lithium ions, the battery life will decay and the capacity will decrease, making it impossible to meet the initial design indicators of the power station. Therefore, realizing the life estimation of lithium batteries in energy storage power stations is an important part of power station management.

[0003] The current life estimation of lithium batteries has the following difficulties: First, a large number of battery cells need to be tested, which involves many application conditions, has a long test cycle, and the test steps are cumbersome, making it impossible to achieve flexible application; Second, using AI technology to establish a neural network and training with a large amount of high-quality test data has high requirements for data. Both of the above require a large amount of test data, which limits the development and application of the battery life SOH algorithm.

[0004] Therefore, the present invention aims to propose a method, product and electronic device for predicting the cycle life of a battery, which can realize the prediction of the cycle life of the battery by using a small amount of test data. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides a method, product and electronic device for predicting the cycle life of a battery.

[0006] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a method for predicting the cycle life of a battery. A method for predicting the cycle life of a battery includes the following steps: Step 1: Select multiple battery cells of the same type for cycle life testing, and record the capacity SOH of each battery cell during the cycle life testing, where each battery cell corresponds to a cycle life test condition; Step 2: After the cycle life testing is completed, select the battery cell with the lowest SOH as the reference battery cell, and use the number of cycles of the reference battery cell as the reference number of cycles or use the cycle time of the reference battery cell as the reference time , and the capacity change as ; Step 3: Select other battery cells as the cells to be tested, obtain the battery capacity change data of the first j cycles of the cells to be tested, and calculate the cycle number difference Cycle_diff or the cycle time difference Time_diff between each cell to be tested and the reference cell at the same capacity; Step 4: Calculate the functional relationship between the cycle number difference Cycle_diff of each cell to be tested and the reference cell or the functional relationship between the cycle time difference Time_diff and the reference cell ; Step 5: Based on the above functional relationship, obtain the relationship formula of Cycle_diff or Time_diff corresponding to the cells to be tested within the reference SOH range ; Step 6: Based on the above relationship formula, calculate the SOH of the cells to be tested, and obtain the cycle number or cycle time of the cells to be tested at different SOHs based on the SOH of the cells to be tested.

[0007] Further, in Step 3, the selecting other battery cells as the cells to be tested, obtaining the battery capacity change data of the first j cycles of the cells to be tested, and calculating the cycle number difference Cycle_diff or the cycle time difference Time_diff between each cell to be tested and the reference cell at the same capacity specifically includes the following steps: Step 31: Establish a functional relationship between the SOH of the cells to be tested and the cycle number or cycle time, with SOH as the independent variable and the cycle number or the cycle time as the dependent variable, then the functional relationship is: or ; After the functional relationship is established, substitute as the independent variable and calculate to obtain the corresponding cycle number or cycle time of the cells to be tested at , that is or ; Step 33: The cycle number difference Cycle_diff or the cycle time difference Time_diff between each cell to be tested and the reference cell at the same capacity, that is: or .

[0008] Further, in Step 4, the calculating the functional relationship between the cycle number difference Cycle_diff of each cell to be tested and the reference cell or the functional relationship between the cycle time difference Time_diff and the reference cell The functional relationship is in the form of a polynomial, exponential, or power-exponential form.

[0009] Further, in step 4, calculating the cycle number difference Cycle_diff or cycle time difference Time_diff of each battery cell to be tested and the reference battery cell The functional relationship requires calculating the parameters to be identified in the function, and the number of values contained in the Cycle_diff or Time_diff array should be greater than or equal to the parameters to be identified in the functional relationship.

[0010] Further, in step 6, calculating the SOH of the battery cell to be tested, that is The corresponding cycle number When added to Cycle_diff, or The corresponding time when added to Time_diff.

[0011] Further, in step 5, the relationship of Cycle_diff or Time_diff corresponding to the battery cell to be tested within the reference SOH range of That is: ; .

[0012] Further, the cycle life test conditions include the current rate and temperature during the charge and discharge process of the battery cell to be tested.

[0013] In a second aspect, the present invention provides a computer program product.

[0014] A computer program product includes a computer program, and when the computer program is executed by a processor, it implements the above-mentioned battery cycle life prediction method.

[0015] In a third aspect, the present invention provides an electronic device.

[0016] An electronic device includes at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor can execute the above-mentioned battery cycle life prediction method.

[0017] In summary, compared with the prior art, the beneficial effects of the above technical solutions are: The present invention can predict the battery cycle life by using a small amount of test data. In the present invention, it is possible to predict the life of other battery cells from the life test data of one battery cell, with less data used, high accuracy, and reduced complexity of battery cell life prediction. Description of the Drawings

[0018] Figure 1 is a flowchart for predicting the battery cycle life; Figure 2 is the cycle life curve of a group of battery cells; Figure 3 is the test curve of the cycle life of two battery cells; Figure 4 is the difference in cycle life of two battery cells under different SOH; Figure 5 is the fitting curve of the difference in cycle life of two battery cells; Figure 6 is the prediction effect of the cycle life of the battery cell. Detailed Implementation Manner

[0019] The principles and features of the present invention will be described below in conjunction with all the drawings. The examples given are only used to explain the present invention and are not intended to limit the scope of the present invention.

[0020] The embodiments of the present invention disclose a method, a product and an electronic device for predicting the battery cycle life.

[0021] In the first aspect, the embodiments of the present invention disclose a method for predicting the battery cycle life.

[0022] Referring to Figures 1-6 , a method for predicting the battery cycle life includes the following steps: Step 1: Select multiple battery cells of the same type for cycle life testing, and record the capacity change of each battery cell during the cycle life testing. Among them, each battery cell corresponds to a cycle life test condition; Step 2: After the cycle life testing is completed, select the battery cell with the lowest SOH as the reference battery cell, and use the number of cycle turns of this battery cell as the reference number of turns or the cycle time as the reference time , and the capacity change as ; Step 3: Select other battery cells as the battery cells to be tested, and obtain the battery capacity change data of the first j turns of the battery cells to be tested, and calculate the difference in the number of cycle turns Cycle_diff or the difference in cycle time Time_diff between each battery cell to be tested and the reference battery cell at the same capacity; Step 4: Calculate the functional relationship between the difference in the number of cycle turns Cycle_diff or the difference in cycle time Time_diff of each battery cell to be tested and the reference battery cell ; Step 5: Based on the above functional relationship, obtain that the battery cells to be tested are within the reference SOH range of The relational expressions for the corresponding Cycle_diff or the cycle time difference Time_diff, namely , or ; Step 6: Based on the above relational expressions, calculate the SOH of the battery cell to be measured, and obtain the number of cycles or cycle time of the battery cell to be measured at different SOHs based on the SOH of the battery cell to be measured.

[0023] A battery cycle life prediction method disclosed in an embodiment of the present invention can realize the prediction of the battery cycle life by using a small amount of test data. In the embodiment of the present invention, it is possible to predict the life of other battery cells from the life test data of one battery cell, with less data used, high accuracy, and reduced complexity of battery cell life prediction.

[0024] The following elaborates on each of the above steps.

[0025] Step 1: Select multiple battery cells of the same type for cycle life testing, and record the capacity change of each battery cell during the cycle life testing. Among them, each battery cell corresponds to a cycle life test condition. The cycle life test condition includes the current rate and temperature during the charge and discharge process of the battery cell to be measured.

[0026] Specifically, select multiple battery cells of the same type for life testing. Each battery cell corresponds to a cycle life test condition, and record the capacity change of each battery cell with the cycle time or the number of cycles during the test, as shown in Table 1. The number of cycles or cycle time of each battery cell may not be the same, but the corresponding SOH of each battery cell needs to be less than the set threshold . The setting of the threshold needs to refer to the SOH attenuation situation of the battery cell. When there is a relatively obvious attenuation in each battery cell and there is a large difference in SOH between each battery cell, the present invention will achieve better results. In the present invention, the threshold can be set to 0.93, that is, the SOH of all battery cells should be less than 0.93.

[0027] Table 1 Test capacity change data of each battery cell

[0028] Step 2: After the cycle life test is completed, select the battery cell with the lowest SOH as the reference battery cell, and use the number of cycles of this battery cell as the reference number of cycles or the cycle time as the reference time and the capacity change as .

[0029] Specifically, Figure 2These are the cycle life test curves of a group of battery cells. Each cell corresponds to different cycle life test conditions, so different capacity life decay trajectories are presented. Since the operating conditions of the battery are more complex during actual use, the capacity decay curve will be more complex. Record the number of cycles and the corresponding SOH value for each cycle life test. Among this group of battery cells, select the cell with the lowest SOH as the reference cell, and then select another cell as the cell to be tested.

[0030] The reference cell is the cell with the lowest SOH, not any random cell. For example, the SOH change range of cell 1 is [1~0.83], and the SOH change range of cell 2 is [1~0.84]. It can be seen that there are no cycle numbers and cycle times for cell 2 when SOH is in the range of [0.84~0.83]. The present invention uses the SOH change data of cell 1 to predict the cycle number or cycle time corresponding to SOH of [0.84 - 0.83] for cell 2. Therefore, it is necessary to select the cell with the lowest SOH.

[0031] Step 3: Select other cells as the cells to be tested, and obtain the battery capacity change data of the first j cycles of the cells to be tested. Calculate the cycle number difference Cycle_diff or cycle time difference Time_diff between each cell to be tested and the reference cell at the same capacity.

[0032] Specifically, among the cells in step 2, after excluding the reference cell, select the other N - 1 cells as the cells to be tested, and take out the battery capacity change data of the first j (j < i) cycles of the cells to be tested. The test curves of the two selected cells are as Figure 3 shown. It can be seen that at the same SOH, the cycle number of the reference cell is more than that of the cell to be tested.

[0033] Calculate the cycle number difference Cycle_diff or cycle time difference Time_diff between each cell to be tested and the reference cell at the same capacity, as shown in Table 2 and Table 3.

[0034] Table 2 Cycle number difference between each cell and the reference cell at the same capacity

[0035] Table 3 Time difference between each cell and the reference cell at the same capacity

[0036] Since there may not necessarily be corresponding cycle numbers or cycle times for the cells to be tested at each time, it is necessary to calculate the corresponding cycle number or cycle time of the cells to be tested at each time. The calculation process is as follows: Step 31: Establish a functional relationship between the SOH of the battery cell to be tested and the number of cycles. Using the SOH as the independent variable and the number of cycles or the cycle time as the dependent variable, the functional relationship is: or ; Step 32: After establishing the functional relationship, use as the independent variable and substitute it into the calculation to obtain the corresponding number of cycles of the battery cell to be tested at or the cycle time , that is or ; For example, when the battery is charged and then discharged once, it is 1 charge-discharge cycle, that is, 1 cycle. If another charge and discharge operation is performed, it is 2 cycles. The cycle time is the running time of the battery. When the battery is charged and then discharged once, it takes 2 hours, so the cycle time is 2 hours.

[0037] Step 33: The difference in the number of cycles Cycle_diff or the difference in cycle time Time_diff between each battery cell to be tested and the reference battery cell at the same capacity, that is: or .

[0038] Step 4: Calculate the functional relationship between the difference in the number of cycles Cycle_diff or the difference in cycle time Time_diff of each battery cell to be tested and the reference battery cell .

[0039] Specifically, select the test data of the reference battery cell and the battery cell to be tested when the SOH is in the range of [0.93 - 1], and calculate the difference in the number of cycles Cycle_diff between the battery cell to be tested and the reference battery cell at the same capacity. The calculation results are as Figure 4 shown. It can be seen from the figure that the difference in the number of cycles of the two battery cells gradually stabilizes as the SOH decreases.

[0040] According to Figure 4 the results, calculate the functional relationship between the difference in the number of cycles Cycle_diff or the difference in cycle time Time_diff of the battery cell to be tested and the reference battery cell , that is or ; It is necessary to calculate the parameters to be identified in the function, and the number of values contained in the Cycle_diff or Time_diff array should be greater than or equal to the parameters to be identified in the functional relationship; The functional relationship in this embodiment is, specifically ; a, b, and c are parameters to be identified. Through data fitting, a = 244.38, b = 38.78, and c = -225.09 are obtained. The fitting result is as shown in Figure 5 , indicating that the fitting curve can fit the variation of the original data with good fitting effect. Thus, we have established the mathematical relationship between the cycle difference and SOH between the reference battery cell and the battery cell to be tested.

[0041] Step 5: Based on the above functional relationship, obtain the relationship formula of Cycle_diff corresponding to the battery cell to be tested within the reference SOH range of , that is .

[0042] Specifically, since the final cycle life test of the reference battery cell is 0.83, and the lowest SOH of the data used in the aforementioned fitting curve is 0.93. Therefore, in order to predict the cycle number of the battery cell to be tested within the SOH range of [0.83 - 0.93], substitute the SOH value of the reference battery cell into the relationship formula to obtain the following formula: .

[0043] Step 6: Based on the above relationship formula, calculate the SOH of the battery cell to be tested, and obtain the cycle number or cycle time of the battery cell to be tested at different SOHs based on the SOH of the battery cell to be tested.

[0044] Specifically, calculate the SOH of the battery cell to be predicted, that is the sum of the cycle number corresponding to and Cycle_diff, or the sum of the cycle number corresponding to and Time_diff. The calculation formula is: ;

[0045] Thus, the cycle number of the battery cell to be tested at different SOHs can be obtained. As shown in Figure 6 , it can be seen from Figure 6 that the life prediction of the battery cell to be tested is almost consistent with the true value, and the prediction effect is good.

[0046] In the second aspect, the embodiment of the present invention also provides a computer program product.

[0047] A computer program product includes a computer program, and when the computer program is executed by a processor, it implements the above-mentioned battery cycle life prediction method.

[0048] In the third aspect, the embodiment of the present invention also provides an electronic device.

[0049] An electronic device includes at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the battery cycle life prediction method described above.

[0050] The foregoing are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for predicting the cycle life of a battery, characterized in that, Including the following steps: Step 1: Select multiple battery cells of the same type for cycle life tests, and record the State of Health (SOH) of each battery cell during the cycle life tests. Among them, each battery cell corresponds to a cycle life test condition; Step 2: After the cycle life test is completed, select the cell with the lowest SOH as the reference cell, and use the number of cycles of the reference cell as the reference number of cycles or use the cycle time of the reference cell as the reference time , and use the capacity change as ; Step 3: Select other battery cells as the battery cells to be tested, and obtain the battery capacity change data of the first j cycles of the battery cells to be tested, and calculate the cycle number difference Cycle_diff or cycle time difference Time_diff between each battery cell to be tested and the reference battery cell at the same capacity; Step 4: Calculate the functional relationship between the cycle number difference Cycle_diff of each cell under test and the reference cell or the functional relationship between the cycle time difference Time_diff and the reference cell ; Step 5: Based on the above functional relationship, obtain the relational expression of Cycle_diff or Time_diff corresponding to the cell under test within the reference SOH range of ; Step 6: Based on the above relationship formula, calculate the SOH of the battery cells to be tested, and obtain the cycle number or cycle time of the battery cells to be tested at different SOH values based on the SOH of the battery cells to be tested.

2. The battery cycle life prediction method according to claim 1, characterized in that In Step 3, the step of selecting other battery cells as the battery cells to be tested, obtaining the battery capacity change data of the first j cycles of the battery cells to be tested, and calculating the cycle number difference Cycle_diff or cycle time difference Time_diff between each battery cell to be tested and the reference battery cell at the same capacity specifically includes the following steps: Step 31: Establish a functional relationship between the SOH of the battery cell under test and the number of cycles or cycle time. Using the SOH as the independent variable and the number of cycles or the cycle time as the dependent variable, the functional relationship is: or ; Step 32: After establishing the functional relationship, substitute as the independent variable and calculate to obtain the number of cycles corresponding to the cell under test at or the cycle time , that is, or ; Step 33: The cycle number difference Cycle_diff or cycle time difference Time_diff between each battery cell to be tested and the reference battery cell at the same capacity, that is: or 。 3. A method for predicting the battery cycle life according to claim 1, characterized in that: In step 4, calculate the function relationship between the cycle number difference Cycle_diff of each battery cell to be measured and the reference battery cell or the function relationship between the cycle time difference Time_diff and the reference battery cell . The function relationship is in polynomial, exponential or power exponential form.

4. A battery cycle life prediction method according to claim 3, characterized in that: In step 4, when calculating the functional relationship between the cycle number difference Cycle_diff or the cycle time difference Time_diff of each cell to be tested and the reference cell , it is necessary to calculate the parameters to be identified in the function, and the number of values contained in the Cycle_diff or Time_diff array should be greater than or equal to the parameters to be identified in the functional relationship.

5. A method for predicting the battery cycle life according to claim 1, characterized in that: In step 6, calculate the SOH of the battery cell to be measured, that is The number of cycles corresponding to The sum of Cycle_diff, or The sum of the time corresponding to and Time_diff.

6. The battery cycle life prediction method according to claim 1, wherein: In step 5, the cell under test is within the reference SOH range of The corresponding relationship formula of Cycle_diff or Time_diff, that is: ； 。 7. A method for predicting the battery cycle life according to claim 1, characterized in that: The cycle life test conditions include the current rate and temperature during the charge and discharge process of the battery cells to be tested.

8. A computer program product, characterized in that: Including a computer program, when the computer program is executed by a processor, it implements a battery cycle life prediction method according to any one of claims 1-7.

9. An electronic device, characterized in that: Including at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a battery cycle life prediction method according to any one of claims 1-7.

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